Stromal Cell Identity Influences the In Vivo Functionality of Engineered Capillary Networks Formed by Co-delivery of Endothelial Cells and Stromal Cells

A major translational challenge in the fields of therapeutic angiogenesis and tissue engineering is the ability to form functional networks of blood vessels. Cell-based strategies to promote neovascularization have been widely explored, and have led to the consensus that co-delivery of endothelial cells (ECs) (or their progenitors) with some sort of a supporting stromal cell type is the most effective approach. However, the choice of stromal cells has varied widely across studies, and their impact on the functional qualities of the capillaries produced has not been examined. In this study, we injected human umbilical vein ECs alone or with normal human lung fibroblasts (NHLFs), human bone marrow-derived mesenchymal stem cells (BMSCs), or human adipose-derived stem cells (AdSCs) in a fibrin matrix into subcutaneous pockets in SCID mice. All conditions yielded new human-derived vessels that inosculated with mouse vasculature and perfused the implant, but there were significant functional differences in the capillary networks, depending heavily on the identity of the co-delivered stromal cells. EC-alone and EC-NHLF implants yielded immature capillary beds characterized by high levels of erythrocyte pooling in the surrounding matrix. EC-BMSC and EC-AdSC implants produced more mature capillaries characterized by less extravascular leakage and the expression of mature pericyte markers. Injection of a fluorescent tracer into the circulation also showed that EC-BMSC and EC-AdSC implants formed vasculature with more tightly regulated permeability. These results suggest that the identity of the stromal cells is key to controlling the functional properties of engineered capillary networks.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140223/1/ten.tea.2012.0281.pd

Assessing the Permeability of Engineered Capillary Networks in a 3D Culture

Many pathologies are characterized by poor blood vessel growth and reduced nutrient delivery to the surrounding tissue, introducing a need for tissue engineered blood vessels. Our lab has developed a 3D co-culture method to grow interconnected networks of pericyte-invested capillaries, which can anastamose with host vasculature following implantation to restore blood flow to ischemic tissues. However, if the engineered vessels contain endothelial cells (ECs) that are misaligned or contain wide junctional gaps, they may function improperly and behave more like the pathologic vessels that nourish tumors. The purpose of this study was to test the resistance to permeability of these networks in vitro, grown with different stromal cell types, as a metric of vessel functionality. A fluorescent dextran tracer was used to visualize transport across the endothelium and the pixel intensity was quantified using a customized MATLAB algorithm. In fibroblast-EC co-cultures, the dextran tracer easily penetrated through the vessel wall and permeability was high through the first 5 days of culture, indicative of vessel immaturity. Beyond day 5, dextran accumulated at the periphery of the vessel, with very little transported across the endothelium. Quantitatively, permeability dropped from initial levels of 61% to 39% after 7 days, and to 7% after 2 weeks. When ECs were co-cultured with bone marrow-derived mesenchymal stem cells (MSCs) or adipose-derived stem cells (AdSCs), much tighter control of permeability was achieved. Relative to the EC-fibroblast co-cultures, permeabilities were reduced 41% for the EC-MSC co-cultures and 50% for the EC-AdSC co-cultures after 3 days of culture. By day 14, these permeabilities decreased by 68% and 77% over the EC-fibroblast cultures. Co-cultures containing stem cells exhibit elevated VE-cadherin levels and more prominent EC-EC junctional complexes when compared to cultures containing fibroblasts. These data suggest the stromal cell identity influences the functionality and physiologic relevance of engineered capillary networks

SARS-CoV-2 breakthrough infections among vaccinated individuals with rheumatic disease : Results from the COVID-19 Global Rheumatology Alliance provider registry

Funding Information: members of the COVID-19 Global Rheumatology Alliance and do not necessarily represent the views of the American College of Rheumatology (ACR), EULAR, the UK National Health Service (NHS), the National Institute for Health Research (NIHR), the UK Department of Health or any other organisation. Competing interests KLH reports she has received non-personal speaker’s fees from AbbVie and grant income from BMS, UCB and Pfizer, all unrelated to this manuscript; KLH is supported by the NIHR Manchester Biomedical Research Centre. LG reports personal consultant fees from AbbVie, Amgen, BMS, Biogen, Celgene, Gilead, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis and UCB, and grants from Amgen, Lilly, Janssen, Pfizer, Sandoz, Sanofi and Galapagos, all unrelated to this manuscript. AS reports research grants from a consortium of 14 companies (among them AbbVie, BMS, Celltrion, Fresenius Funding Information: Kabi, Gilead/Galapagos, Lilly, Mylan/Viatris, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis and UCB) supporting the German RABBIT register and personal fees from lectures for AbbVie, MSD, Roche, BMS, Lilly and Pfizer, all unrelated to this manuscript. LC has not received fees or personal grants from any laboratory, but her institute works by contract for laboratories among other institutions, such as AbbVie Spain, Eisai, Gebro Pharma, Merck Sharp & Dohme España, Novartis Farmaceutica, Pfizer, Roche Farma, Sanofi-Aventis, Astellas Pharma, Actelion Pharmaceuticals España, Grünenthal and UCB Pharma. EF-M reports personal consultant fees from Boehringer Ingelheim Portugal and that LPCDR received support for specific activities: grants from AbbVie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal, MSD, Celgene, Medac, Pharmakern and GAfPA; grants and non-financial support from Pfizer; and non-financial support from Grünenthal, outside the submitted work. IB reports personal consultant fees from AbbVie, Novartis, Pfizer and Janssen, all unrelated to this manuscript. JZ reports speaker fees from AbbVie, Novartis and Janssen/Johnson & Johnson, all unrelated to this manuscript. GR-C reports personal consultant fees from Eli Lilly and Novartis, all unrelated to this manuscript. JS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers: R01 AR077607, P30 AR070253 and P30 AR072577), and the R Bruce and Joan M Mickey Research Scholar Fund. JS has received research support from Amgen and Bristol Myers Squibb and performed consultancy for Bristol Myers Squibb, Gilead, Inova, Janssen and Optum, unrelated to this work. LW receives speaker’s bureau fees from Aurinia Pharma, unrelated to this manuscript. SB reports no competing interests related to this work. He reports non-branded consulting fees for AbbVie, Horizon and Novartis (all <$10 000). MGM has no competing interests related to this work. She serves as a patient consultant for BMS, BI JNJ and Aurinia (all <$10 000). RG reports no competing interests related to this work. Outside of this work she reports personal and/or speaking fees from AbbVie, Janssen, Novartis, Pfizer and Cornerstones and travel assistance from Pfizer (all <$10 000). JH reports no competing interests related to this work. He is supported by grants from the Rheumatology Research Foundation and has salary support from the Childhood Arthritis and Rheumatology Research Alliance. He has performed consulting for Novartis, Sobi and Biogen, all unrelated to this work (<$10 000). ESi reports non-financial support from Canadian Arthritis Patient Alliance, outside the submitted work. PS reports personal fees from the American College of Rheumatology/Wiley Publishing, outside the submitted work. ZW reports grant support from Bristol Myers Squibb and Principia/Sanofi and performed consultancy for Viela Bio and MedPace, outside the submitted work. His work is supported by grants from the National Institutes of Health. PMM has received consulting/speaker’s fees from AbbVie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this study. PMM is supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC). PCR reports no competing interests related to this work. Outside of this work PCR reports personal fees from AbbVie, Atom Bioscience, Eli Lilly, Gilead, GlaxoSmithKline, Janssen, Kukdong, Novartis, UCB, Roche and Pfizer; meeting attendance support from BMS, Pfizer and UCB; and grant funding from Janssen, Novartis, Pfizer and UCB Pharma (all <$10 000). JY reports no competing interests related to this work. Her work is supported by grants from the National Institutes of Health (K24 AR074534 and P30 AR070155). Outside of this work, she has received research grants or performed consulting for Gilead, BMS Foundation, Pfizer, Aurinia and AstraZeneca. Funding Information: Twitter Jean Liew @rheum_cat, Loreto Carmona @carmona_loreto, Pedro M Machado @pedrommcmachado and Philip C Robinson @philipcrobinson Contributors All authors contributed to the study design, data collection, interpretation of results and review/approval of the final submitted manuscript. JL and MG are guarantors for this manuscript. Funding MG reports grants from the National Institutes of Health, NIAMS, outside the submitted work. KLH is supported by the NIHR Manchester Biomedical Research Centre. JS is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers: R01 AR077607, P30 AR070253 and P30 AR072577), and the R Bruce and Joan M Mickey Research Scholar Fund. JH is supported by grants from the Rheumatology Research Foundation. ZW is supported by grants from the National Institutes of Health. PMM is supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC). JY is supported by grants from the National Institutes of Health (K24 AR074534 and P30 AR070155). Publisher Copyright: ©Objective. While COVID-19 vaccination prevents severe infections, poor immunogenicity in immunocompromised people threatens vaccine effectiveness. We analysed the clinical characteristics of patients with rheumatic disease who developed breakthrough COVID-19 after vaccination against SARS-CoV-2.  Methods. We included people partially or fully vaccinated against SARS-CoV-2 who developed COVID-19 between 5 January and 30 September 2021 and were reported to the Global Rheumatology Alliance registry. Breakthrough infections were defined as occurring ≥14 days after completion of the vaccination series, specifically 14 days after the second dose in a two-dose series or 14 days after a single-dose vaccine. We analysed patients' demographic and clinical characteristics and COVID-19 symptoms and outcomes. Results SARS-CoV-2 infection was reported in 197 partially or fully vaccinated people with rheumatic disease (mean age 54 years, 77% female, 56% white). The majority (n=140/197, 71%) received messenger RNA vaccines. Among the fully vaccinated (n=87), infection occurred a mean of 112 (±60) days after the second vaccine dose. Among those fully vaccinated and hospitalised (n=22, age range 36-83 years), nine had used B cell-depleting therapy (BCDT), with six as monotherapy, at the time of vaccination. Three were on mycophenolate. The majority (n=14/22, 64%) were not taking systemic glucocorticoids. Eight patients had pre-existing lung disease and five patients died. Conclusion. More than half of fully vaccinated individuals with breakthrough infections requiring hospitalisation were on BCDT or mycophenolate. Further risk mitigation strategies are likely needed to protect this selected high-risk population.publishersversionPeer reviewe

Training future generations to deliver evidence-based conservation and ecosystem management

1. To be effective, the next generation of conservation practitioners and managers need to be critical thinkers with a deep understanding of how to make evidence-based decisions and of the value of evidence synthesis. 2. If, as educators, we do not make these priorities a core part of what we teach, we are failing to prepare our students to make an effective contribution to conservation practice. 3. To help overcome this problem we have created open access online teaching materials in multiple languages that are stored in Applied Ecology Resources. So far, 117 educators from 23 countries have acknowledged the importance of this and are already teaching or about to teach skills in appraising or using evidence in conservation decision-making. This includes 145 undergraduate, postgraduate or professional development courses. 4. We call for wider teaching of the tools and skills that facilitate evidence-based conservation and also suggest that providing online teaching materials in multiple languages could be beneficial for improving global understanding of other subject areas.Peer reviewe

Results From the Global Rheumatology Alliance Registry

Funding Information: We acknowledge financial support from the ACR and EULAR. The ACR and EULAR were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Publisher Copyright: © 2022 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.Objective: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. Methods: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. Results: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. Conclusion: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.publishersversionepub_ahead_of_prin

Associations of baseline use of biologic or targeted synthetic DMARDs with COVID-19 severity in rheumatoid arthritis : Results from the COVID-19 Global Rheumatology Alliance physician registry

Funding Information: Competing interests JAS is supported by the National Institute of Arthritis and Funding Information: Musculoskeletal and Skin Diseases (grant numbers K23 AR069688, R03 AR075886, L30 AR066953, P30 AR070253 and P30 AR072577), the Rheumatology Research Foundation (K Supplement Award and R Bridge Award), the Brigham Research Institute, and the R Bruce and Joan M Mickey Research Scholar Fund. JAS has received research support from Amgen and Bristol-Myers Squibb and performed consultancy for Bristol-Myers Squibb, Gilead, Inova, Janssen and Optum, unrelated to this work. ZSW reports grant support from Bristol-Myers Squibb and Principia/ Sanofi and performed consultancy for Viela Bio and MedPace, outside the submitted work. His work is supported by grants from the National Institutes of Health. MG is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers K01 AR070585 and K24 AR074534; JY). KLH reports she has received speaker’s fees from AbbVie and grant income from BMS, UCB and Pfizer, all unrelated to this study. KLH is also supported by the NIHR Manchester Biomedical Research Centre. LC has not received fees or personal grants from any laboratory, but her institute works by contract for laboratories such as, among other institutions, AbbVie Spain, Eisai, Gebro Pharma, Merck Sharp & Dohme España, Novartis Farmaceutica, Pfizer, Roche Farma, Sanofi Aventis, Astellas Pharma, Actelion Pharmaceuticals España, Grünenthal and UCB Pharma. LG reports research grants from Amgen, Galapagos, Janssen, Lilly, Pfizer, Sandoz and Sanofi; consulting fees from AbbVie, Amgen, BMS, Biogen, Celgene, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi Aventis and UCB, all unrelated to this study. EFM reports that LPCDR received support for specific activities: grants from AbbVie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal, MSD, Celgene, Medac, Pharma Kern and GAfPA; grants and non-financial support from Pfizer; and non-financial support from Grünenthal, outside the submitted work. AS reports grants from a consortium of 13 companies (among them AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi Aventis and UCB) supporting the German RABBIT register, and personal fees from lectures for AbbVie, MSD, Roche, BMS and Pfizer, outside the submitted work. AD-G has no disclosures relevant to this study. His work is supported by grants from the Centers for Disease Control and Prevention and the Rheumatology Research Foundation. KMD is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (T32-AR-007258) and the Rheumatology Research Foundation. NJP is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (T32-AR-007258). PD has received research support from Bristol-Myers Squibb, Chugai and Pfizer, and performed consultancy for Boehringer Ingelheim, Bristol-Myers Squibb, Lilly, Sanofi, Pfizer, Chugai, Roche and Janssen, unrelated to this work. NS is supported by the RRF Investigator Award and the American Heart Association. MFU-G reports grant support from Janssen and Pfizer. SB reports no competing interests related to this work. He reports non-branded consulting fees for AbbVie, Horizon, Novartis and Pfizer (all <$10 000). RG reports no competing interests related to this work. Outside of this work she reports personal and/or speaking fees from AbbVie, Janssen, Novartis, Pfizer and Cornerstones, and travel assistance from Pfizer (all <$10 000). JH reports no competing interests related to this work. He is supported by grants from the Rheumatology Research Foundation and the Childhood Arthritis and Rheumatology Research Alliance. He has performed consulting for Novartis, Sobi and Biogen, all unrelated to this work (<$10 000). JL has received research funding from Pfizer, outside the submitted work. ES is a Board Member of the Canadian Arthritis Patient Alliance, a patient-run, volunteer-based organisation whose activities are largely supported by independent grants from pharmaceutical companies. PS reports no competing interests related to this work. He reports honorarium for doing social media for American College of Rheumatology journals (<$10 000). PMM has received consulting/speaker’s fees from AbbVie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all <$10 000). PMM is supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC). PCR reports no competing interests related to this work. Outside of this work he reports personal consulting and/or speaking fees from AbbVie, Eli Lilly, Janssen, Novartis, Pfizer and UCB, and travel assistance from Roche (all <$10 000). JY reports no competing interests related to this work. Her work is supported by grants from the National Institutes of Health, Centers for Disease Control, and the Agency for Healthcare Research and Quality. She has performed consulting for Eli Lilly and AstraZeneca, unrelated to this project. Publisher Copyright: © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.Objective To investigate baseline use of biologic or targeted synthetic (b/ts) disease-modifying antirheumatic drugs (DMARDs) and COVID-19 outcomes in rheumatoid arthritis (RA). Methods We analysed the COVID-19 Global Rheumatology Alliance physician registry (from 24 March 2020 to 12 April 2021). We investigated b/tsDMARD use for RA at the clinical onset of COVID-19 (baseline): abatacept (ABA), rituximab (RTX), Janus kinase inhibitors (JAKi), interleukin 6 inhibitors (IL-6i) or tumour necrosis factor inhibitors (TNFi, reference group). The ordinal COVID-19 severity outcome was (1) no hospitalisation, (2) hospitalisation without oxygen, (3) hospitalisation with oxygen/ventilation or (4) death. We used ordinal logistic regression to estimate the OR (odds of being one level higher on the ordinal outcome) for each drug class compared with TNFi, adjusting for potential baseline confounders. Results Of 2869 people with RA (mean age 56.7 years, 80.8% female) on b/tsDMARD at the onset of COVID-19, there were 237 on ABA, 364 on RTX, 317 on IL-6i, 563 on JAKi and 1388 on TNFi. Overall, 613 (21%) were hospitalised and 157 (5.5%) died. RTX (OR 4.15, 95% CI 3.16 to 5.44) and JAKi (OR 2.06, 95% CI 1.60 to 2.65) were each associated with worse COVID-19 severity compared with TNFi. There were no associations between ABA or IL6i and COVID-19 severity. Conclusions People with RA treated with RTX or JAKi had worse COVID-19 severity than those on TNFi. The strong association of RTX and JAKi use with poor COVID-19 outcomes highlights prioritisation of risk mitigation strategies for these people.publishersversionPeer reviewe

Combinatorial hydrogel library enables identification of materials that mitigate the foreign body response in primates

The foreign body response is an immune-mediated reaction that can lead to the failure of implanted medical devices and discomfort for the recipient. There is a critical need for biomaterials that overcome this key challenge in the development of medical devices. Here we use a combinatorial approach for covalent chemical modification to generate a large library of variants of one of the most widely used hydrogel biomaterials, alginate. We evaluated the materials in vivo and identified three triazole-containing analogs that substantially reduce foreign body reactions in both rodents and, for at least 6 months, in non-human primates. The distribution of the triazole modification creates a unique hydrogel surface that inhibits recognition by macrophages and fibrous deposition. In addition to the utility of the compounds reported here, our approach may enable the discovery of other materials that mitigate the foreign body response.Leona M. and Harry B. Helmsley Charitable Trust (3-SRA-2014-285-M-R)United States. National Institutes of Health (EB000244)United States. National Institutes of Health (EB000351)United States. National Institutes of Health (DE013023)United States. National Institutes of Health (CA151884)United States. National Institutes of Health (P41EB015871-27)National Cancer Institute (U.S.) (P30-CA14051

Pixel comparison algorithm.

<p>In Panel A, the algorithm makes a 3D overlay of the images of each channel. The user defines the location of a background box. In Panel B, each green image from the 3D z-stack is depicted in gray scale. In Panel C, the maximum projection of the green z-stack is converted to a binary image after the background (defined in Panel A) is eliminated. In Panel D, the portion of the red 3D z-stack that corresponds to the same area depicted in the green image is shown in gray scale as a maximum projection. In Panel E, only those pixels from the red image in Panel D that overlap with the green pixels in Panel C are shown. The pixel intensities from the image in Panel E are tallied and output as a ratio of red:green.</p

Quantification of permeability using a customized algorithm.

<p>Quantification generated via the customized MATLAB algorithm to determine the amount of Texas Red-conjugated dextran within the capillary interior at each time point. An isolated * symbol indicates statistical significance (p<0.05) between data points (NHLF vs. MSC vs. AdSC) at any one particular time point, whereas a bar beneath the * symbol indicates statistical significance (p<0.05) across all 3 experimental groups at each time point. The +, #, and % symbols indicate statistically significant differences (p<0.005) between the data marked by these symbols at certain time points and all other time points marked with different symbols.</p